Sammenlign metoder

	Fourier glidende gjennomsnitt (Fourier MA) modell	ARIMA-modell (Autoregressiv Integrert Glidende Gjennomsnitt)
Fagfelt	Økonometri	Økonometri
Familie	Regression model	Regression model
Opprinnelsesår	1990s–2000s	1970
Opphavsperson	Harvey, A. C.; Hyndman, R. J.	George Box and Gwilym Jenkins
Type	Time series model	Time series forecasting model
Opprinnelig kilde	Hyndman, R. J., & Athanasopoulos, G. (2021). Forecasting: Principles and Practice (3rd ed.). OTexts.	Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day.
Alias	Fourier MA, Fourier-augmented moving average, trigonometric MA model, harmonic moving average model	ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q)
Relaterte	2	6
Sammendrag	The Fourier MA model combines a Moving Average (MA) error structure with Fourier series terms — sine and cosine pairs — to capture complex or high-frequency seasonal patterns in time series data. It is particularly useful when the seasonal period is long or irregular, making classical seasonal ARIMA parameterisation infeasible.	The ARIMA(p,d,q) model is the standard workhorse for univariate time series forecasting. It combines autoregressive terms (past values), differencing to induce stationarity, and moving average terms (past shocks) into a unified linear framework. Developed by Box and Jenkins (1970), it remains one of the most widely applied models in econometrics and applied statistics.